USING COLLECTIVE LEARNING TO SPOT ILLEGAL ACTIVITIES IN BANK CARD ACTIVITIES

August 2025
Vol-11, Issue-4
Paper ID: 27415
ISSN: 2395-4396
Downloads: 0

Abstract & Details

Research Area
Computer Engineering
Keywords
Credit card fraud detection deterministic environments cybersecurity fraud pattern recognition XGBoost Random Forest Naive Bayes Sparkov dataset real-world transaction data financial security model performance metrics accuracy precision recall F1 score synthetic data F1 score and transaction authentication.
Abstract
Detecting fraudulent credit card transactions remains a critical challenge for financial institutions. Since every transaction must pass an authentication process, near is a risk that attackers could impersonate legitimate cardholders to transport out unauthorized activities. This study explores the efficacy of ensemble learning techniques in recognising credit card scam using two datasets: the synthetic Sparkov dataset and a real-world dataset with transaction histories from customers in the European Union. XGBoost, Random Forest, and Naive Bayes classifiers are among the models that are assessed; performance is gauged by accuracy, precision, recall, and F1 score. The findings reveal that most ensemble models demonstrate high performance on the real-world dataset but struggle significantly with the synthetic one. This dissimilarity recommends that, while fraud patterns in real data can be effectively captured in deterministic environments, simulated datasets lack the complexity and unpredictability of real-world transactions. The study also highlights that rigid determinism and limited randomness may increase the jeopardy of credit card information being compromised.
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

# Name Institute / Affiliation
1 PRAJWAL R K T JOHN INSTITUTE OF TECHNOLOGY
2 Mr. S Senthil Murugan T JOHN INSTITUTE OF TECHNOLOGY

How to Cite

Use the following formats to cite this article in your research.

APA Style
K, PRAJWAL R & Murugan, Mr. S Senthil (2025). USING COLLECTIVE LEARNING TO SPOT ILLEGAL ACTIVITIES IN BANK CARD ACTIVITIES. International Journal of Advance Research and Innovative Ideas In Education, 11(4), 3945-3951.
MLA Style
K, PRAJWAL R, and Mr. S Senthil Murugan. "USING COLLECTIVE LEARNING TO SPOT ILLEGAL ACTIVITIES IN BANK CARD ACTIVITIES." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, 2025, pp. 3945-3951.
IEEE Style
PRAJWAL R K and Mr. S Senthil Murugan, "USING COLLECTIVE LEARNING TO SPOT ILLEGAL ACTIVITIES IN BANK CARD ACTIVITIES," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, pp. 3945-3951, 2025.
Vancouver Style
K PRAJWAL R, Murugan Mr. S Senthil. USING COLLECTIVE LEARNING TO SPOT ILLEGAL ACTIVITIES IN BANK CARD ACTIVITIES. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(4):3945-3951.
Harvard Style
K, PRAJWAL R & Murugan, Mr. S Senthil (2025) 'USING COLLECTIVE LEARNING TO SPOT ILLEGAL ACTIVITIES IN BANK CARD ACTIVITIES', International Journal of Advance Research and Innovative Ideas In Education, 11(4), pp. 3945-3951.
Chicago Style
K, PRAJWAL R and Mr. S Senthil Murugan. "USING COLLECTIVE LEARNING TO SPOT ILLEGAL ACTIVITIES IN BANK CARD ACTIVITIES." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 3945-3951.
Turabian Style
K, PRAJWAL R and Mr. S Senthil Murugan. "USING COLLECTIVE LEARNING TO SPOT ILLEGAL ACTIVITIES IN BANK CARD ACTIVITIES." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 3945-3951.

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